The Greenpeace organization is gearing up to deliver a presentation to the Ministry of Energy in Zimbabwe, aiming to raise awareness about the significance of renewable energy in mitigating CO2 emissions. They have turned to your team for invaluable assistance in this mission. Your team leader has placed the responsibility on your shoulders to delve into the CO2 emissions dataset and extracting insightful historical emissions trends. Your task also involves crafting an optimized energy allocation plan for Zimbabwe. Presently, Zimbabwe's energy landscape is outlined as follows: The nation's total energy consumption (TED) stands at 51.6 million barrels of oil equivalent (Mboe), with an untapped potential for harnessing renewable energy (PERE) estimated at 13.4 Mboe. Furthermore, Zimbabwe's natural gas demand surpasses its oil demand by a factor of 1.7. Zimbabwe's energy portfolio comprises a diverse array of eight energy sources. The ensuing table furnishes the CO2 emissions coefficients, quantifying emissions in tons of CO2 per Mboe, encompassing both direct and indirect emissions, for these eight energy sources.

Energy Resource	CO2 emissions coefficients ton CO2/Mboe
Direct	Indirect
Oil	1196800	64600
Natural gas	812600	221000
Coal	1507900	149600
Nuclear		49300
Hydropower		18700
Geothermal		25500
Solar		90100
Wind		49300

Optimize the Co2 Emissions:

1. Determine the decision Variables.
2. Determine your objective function and your constraints.
3. Formulate a LP model and solve it using Python.
4. Determine the optimal allocation of energy source in Zimbabwe.